Inverse design, fabrication, and tolerance to extreme environments of radiative cooling coating

Abstract

Alternative to the traditional force cooling technologies, daytime radiative cooling (DRC) has drawn widespread attention for its zero-power. A porous polymer coating based on poly (vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) has been reported as it has excellent DRC capacity. However, performance of the PVDF-HFP coating is affected substantially by its preparation conditions, restricting its application. To resolve the issue, we utilize an artificial neural network (ANN) to predict its DRC capacity and obtain the best preparation condition by siftings. In this work, the predicted solar reflectance ( R ¯ s o l a r ) and emittance of the atmospheric transmittance window ( ε ¯ a t w ), under the optimal preparation condition, reach 0.983 and 0.932, with a 1.865% and 0.107% error from the experimental value, correspondingly. Noticeably, the optimal PVDF-HFP coating achieves about 6℃ temperature drops below ambient temperature during daytime. In addition, to extend its applications in space, we conduct the extreme environmental experiment on the PVDF-HFP coating. After exposing in the extreme environment, R ¯ s o l a r of the coating has degradation rate over 11%. Consequently, these simulative methods and experimental results provide a positive direction for fabricating the high-performance DRCs.